---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: masked
    dtype: string
  - name: label
    dtype: string
  - name: name
    dtype: string
  - name: pronoun
    dtype: string
  - name: pronoun_count
    dtype: int64
  splits:
  - name: train
    num_bytes: 5047914
    num_examples: 23653
  - name: validation
    num_bytes: 144116
    num_examples: 675
  - name: test
    num_bytes: 579900
    num_examples: 2703
  download_size: 3176580
  dataset_size: 5771930
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
language:
- en
tags:
- NLP
- MLM
pretty_name: GENTER
---
# GENTER (GEnder Name TEmplates with pRonouns)

<!-- Provide a quick summary of the dataset. -->

This dataset consists of template sentences associating first names (`[NAME]`) with third-person singular pronouns (`[PRONOUN]`), e.g.,
```
[NAME] asked , not sounding as if [PRONOUN] cared about the answer .
after all , [NAME] was the same as [PRONOUN] 'd always been .
there were moments when [NAME] was soft , when [PRONOUN] seemed more like the person [PRONOUN] had been .
```


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
This dataset is a filtered version of [BookCorpus](https://huggingface.co./datasets/bookcorpus/bookcorpus) containing only sentences where a first name is followed by its correct third-person singular pronoun (he/she). 
Based on these sentences, template sentences (`masked`) are created including two template keys: `[NAME]` and `[PRONOUN]`. Thus, this dataset can be used to generate various sentences with varying names (e.g., from [aieng-lab/namexact](https://huggingface.co./datasets/aieng-lab/namexact)) and filling in the correct pronoun for this name. 


This dataset is a filtered version of [BookCorpus](https://huggingface.co./datasets/bookcorpus/bookcorpus) that includes only sentences where a first name appears alongside its correct third-person singular pronoun (*he*/*she*).  

From these sentences, template-based sentences (`masked`) are created with two template keys: `[NAME]` and `[PRONOUN]`. This design allows the dataset to generate diverse sentences by varying the names (e.g., using names from [aieng-lab/namexact](https://huggingface.co./datasets/aieng-lab/namexact)) and inserting the appropriate pronoun for each name.  


### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [github.com/aieng-lab/gradiend](https://github.com/aieng-lab/gradiend)
- **Paper:** [![arXiv](https://img.shields.io/badge/arXiv-2502.01406-blue.svg)](https://arxiv.org/abs/2502.01406)
- **Original Data**: [BookCorpus](https://huggingface.co./datasets/bookcorpus/bookcorpus)


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

- `text`: the original entry of BookCorpus
- `masked`: the masked version of `text`, i.e., with template masks for the name (`[NAME]`) and the pronoun (`[PRONOUN]`)
- `label`: the gender of the original used name (`F` for female and `M` for male)
- `name`: the original name in `text` that is masked in `masked` as `[NAME]`
- `pronoun`: the original pronoun in `text` that is masked in `masked` as `[PRONOUN]` (*he*/ *she*)
- `pronoun_count`: the number of occurrences of pronouns (typically 1, at most 4)

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

For the training of a gender bias [GRADIEND model](https://github.com/aieng-lab/gradiend), a diverse dataset associating first names with both, its factual and counterfactual pronoun associations, to assess gender-related gradient information.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

The dataset is derived from [BookCorpus](https://huggingface.co./datasets/bookcorpus/bookcorpus) by filtering it and extracting the template structure.

<!-- BookCorpus is a dataset originally collected for academic purposes. The original dataset does not have an explicit license and was constructed by scraping publicly available books from the web. Use of this dataset is limited to **non-commercial research purposes only**. Proper attribution to the original BookCorpus creators is required. -->

We selected BookCorpus as foundational dataset due to its focus on fictional narratives where characters are often referred to by their first names. In contrast, the [English Wikipedia](https://huggingface.co./datasets/wikimedia/wikipedia), also commonly used for the training of transformer models, was less suitable for our purposes. For instance, sentences like *[NAME] Jackson was a musician, [PRONOUN] was a great singer* may be biased towards the name *Michael*.

### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

We filter the entries of BookCorpus and include only sentences that meet the following criteria:

- Each sentence contains at least 50 characters
- Exactly one name of [aieng-lab/namexact](https://huggingface.co./datasets/aieng-lab/namexact) is contained, ensuringa correct name match.
- No other names from a larger name dataset ([aieng-lab/namextend](https://huggingface.co./datasets/aieng-lab/namextend)) are included, ensuring that only a single name appears in the sentence.
- The correct name's gender-specific third-person pronoun (*he* or *she*) is included at least once.
- All occurrences of the pronoun appear after the name in the sentence.
- The counterfactual pronoun does not appear in the sentence.
- The sentence excludes gender-specific reflexive pronouns (*himself*, *herself*) and possesive pronouns (*his*, *her*, *him*, *hers*)
- Gendered nouns (e.g., *actor*, *actress*, ...) are excluded, based on a [gemdered-word dataset](https://github.com/ecmonsen/gendered_words) with 2421 entries.

This approach generated a total of 83772 sentences. To further enhance data quality, we employed s imple BERT model ([bert-base-uncased](https://huggingface.co./google-bert/bert-base-uncased)) as a judge model. This model must predict the correct pronoun for selected names with high certainty, otherwise, sentences may contain noise or ambiguous terms not caught by the initial filtering. Specifically, we used 50 female and 50 male names from the ([aieng-lab/namextend](https://huggingface.co./datasets/aieng-lab/namextend)) train split, and a correct prediction means the correct pronoun token is predicted as the token with the highest probability in the induced Masked Language Modeling (MLM) task. Only sentences for which the judge model correctly predicts the pronoun for every test case were retrained, resulting in a total of 27031 sentences. 

The data is split into training (87.5%), validation (2.5%) and test (10%) subsets.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Due to BookCorpus, only lower-case sentences are contained.


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{drechsel2025gradiendmonosemanticfeaturelearning,
      title={{GRADIEND}: Monosemantic Feature Learning within Neural Networks Applied to Gender Debiasing of Transformer Models}, 
      author={Jonathan Drechsel and Steffen Herbold},
      year={2025},
      eprint={2502.01406},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.01406}, 
}
```


## Dataset Card Authors

[jdrechsel](https://huggingface.co./jdrechsel)